Computed Tomographic Colonography (CTC) is a minimally invasive technique that employs X-Ray CT imaging of the abdomen and pelvis following cleansing and air insufflation of the colon. Originally proposed in the early 1980's, it became practical in the early 1990's following the introduction of helical CT and advances in computer graphics. Currently available multi-slice helical X-Ray CT scanners are capable of producing hundreds of high-resolution (<1 mm cubic voxel) images in a single breath hold. Conventional examination of these source images is rather time-consuming and the detection accuracy is unavoidably limited by human factors such as attention span and eye fatigue. Several visualization and navigation techniques have been proposed to help the radiologists. However, computer aided detection (CAD) tools are envisioned to improve the efficiency and the accuracy beyond what can be achieved by visualization techniques alone. Several studies have investigated CAD for CTC. Vining et al. used abnormal colon wall thickness to detect colonic polyps (See e.g. Vining et al. (1999) in a paper entitled “Virtual colonoscopy with computer-assisted polyp detection” and published in Computer-Aided Diagnosis in Medical Imaging pp. 445-452, Amsterdam, Netherlands: Elsevier Science B. V.). Summers et al. used the mean, gaussian and principal curvatures of the colon surface and showed good preliminary results for phantom and patient data (See e.g. Summers et al. (2000) in a paper entitled “Automated polyp detector for CT colonography: Feasibility study” and published in Radiology 216:284-290; Summers et al. (2001) in a paper entitled “Automated polyp detection at CT colonography: Feasibility assessment in a human population” and published in Radiology 219:51-59). Kiss et al. used surface normals along with sphere fitting (Kiss et al. (2002) in a paper entitled “Computer-aided diagnosis in virtual colonography via combination of surface normal and sphere fitting methods” and published in European Radiology 12:77-81), while Yoshida et al. used both the pre-segmented surface differential characteristics captured by a shape index, and the gradient vector field of the CT data (Yoshida et al. (2001) in a paper entitled “Three-dimensional computer-aided diagnosis scheme for detection of colonic polyps” and published in IEEE Transactions on Medical Imaging 20:1261-1274- Yoshida et al. (2002) in a paper entitled “Computerized detection of colonic polyps at CT colonography on the basis of volumetric features: Pilot study” and published in Radiology 222:327-336). Paik et al. proposed the surface normal overlap algorithm based on the observation that for locally spherical and hemispherical structures, large numbers of surface normals intersect near the centers of these structures (See U.S. Published Patent Application No. 2002-0164061 published on Nov. 7, 2002, which is assigned to the same assignee as the present invention). To improve specificity, Gokturk et al. used triples of randomly oriented orthogonal cross-sectional images of pre-detected suspicious structures which are then classified by support vector machines (See U.S. Published Patent Application No. 2004-0165767 published on Aug. 26, 2004, which is assigned to the same assignee as the present invention), while Acar et al. modeled the way radiologists utilize 3D information as they are examining a stack of 2D images (See U.S. Published Patent Application No. 2004-0141638 published on Jul. 22, 2004, which is assigned to the same assignee as the present invention). Even though a lot of progress has been made in this art, there is still a need to develop new methods to detect and characterize polyps, especially in 3D.